Population Distribution Over Time: How Spatial Distance Matters

2013 ◽  
Author(s):  
Ilenia Epifani ◽  
Rosella Nicolini
Keyword(s):  
Population Distribution ◽  
Spatial Distance ◽  
Over Time

scholarly journals Spinal Cord Injury due to Tumour or Metastasis in Aragón, Northeastern Spain (1991–2008): Incidence, Time Trends, and Neurological Function

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Maayken Elizabeth Louise van den Berg ◽  
Juan M. Castellote ◽  
Jose Ignacio Mayordomo ◽  
Ignacio Mahillo-Fernandez ◽  
Jesus de Pedro-Cuesta
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Population Distribution ◽  
Primary Tumour ◽  
Temporal Trends ◽  
Health Resources ◽  
The Elderly ◽  
Incidence Rates ◽  
Cord Injury ◽  
Over Time

Purpose. Understanding the presentation of spinal cord injury (SCI) due to tumours considering population distribution and temporal trends is key to managing SCI health services. This study quantified incidence rates, function scores, and trends of SCI due to tumour or metastasis over an 18-year time period in a defined region in Spain. Methods. A retrospective cohort study included in-and outpatients with nontraumatic SCI due to tumour or metastasis admitted to a metropolitan hospital in Spain between 1991 and 2008. Main outcome measures were crude and age- and sex-adjusted incidence rates, tumour location and type, distribution by spinal level, neurological level of injury, and impairment ASIA scores. Results. Primary tumour or metastasis accounted for 32.5% of nontraumatic SCI with an incidence rate of 4.1 per million population. Increasing rates with age and over time were observed. Major pathology groups were intradural-extramedullary masses from which meningiomas and neurinomas accounted for 40%. Lesions were mostly incomplete with predominant ASIA Grade D. Conclusions. Increasing incidence rates of tumour-related SCI over time in the middle-aged and the elderly suggest a growing need for neurooncology health resources in the future.

scholarly journals Dynamic Ecological Inference for Time-Varying Population Distributions Based on Sparse, Irregular, and Noisy Marginal Data

2019 ◽  
Vol 27 (3) ◽  
pp. 388-396 ◽  
Author(s):  
Devin Caughey ◽  
Mallory Wang
Keyword(s):  
Population Distribution ◽  
Auxiliary Information ◽  
Population Data ◽  
Target Population ◽  
Small Subset ◽  
Ecological Inference ◽  
Joint Distributions ◽  
Marginal Data ◽  
Time Periods ◽  
Over Time

Social scientists are frequently interested in how populations evolve over time. Creating poststratification weights for surveys, for example, requires information on the weighting variables’ joint distribution in the target population. Typically, however, population data are sparsely available across time periods. Even when population data are observed, the content and structure of the data—which variables are observed and whether their marginal or joint distributions are known—differ across time, in ways that preclude straightforward interpolation. As a consequence, survey weights are often based only on the small subset of auxiliary variables whose joint population distribution is observed regularly over time, and thus fail to take full advantage of auxiliary information. To address this problem, we develop a dynamic Bayesian ecological inference model for estimating multivariate categorical distributions from sparse, irregular, and noisy data on their marginal (or partially joint) distributions. Our approach combines (1) a Dirichlet sampling model for the observed margins conditional on the unobserved cell proportions; (2) a set of equations encoding the logical relationships among different population quantities; and (3) a Dirichlet transition model for the period-specific proportions that pools information across time periods. We illustrate this method by estimating annual U.S. phone-ownership rates by race and region based on population data irregularly available between 1930 and 1960. This approach may be useful in a wide variety of contexts where scholars wish to make dynamic ecological inferences about interior cells from marginal data. A new R package estsubpop implements the method.

scholarly journals Person, Place, Time and COVID-19 Inequities

2021 ◽  
Author(s):  
Alina S Schnake-Mahl ◽  
Usama Bilal
Keyword(s):  
Population Distribution ◽  
Reciprocal Relationship ◽  
Mortality Data ◽  
County Level ◽  
Systemic Racism ◽  
Relational Approach ◽  
Johns Hopkins University ◽  
Race Ethnicity ◽  
Over Time ◽  
Do So

Abstract In their commentary, Zalla et al. argue that the approach taken by Centers for Disease Control (CDC) comparing the proportion of COVID-19 deaths by race/ethnicity to a weighted population distribution ignores how systemic racism structures the composition of places. While the CDC has abandoned their measure, they do so because of the changing geographic distribution of COVID-19, not because the measure underestimates racial disparities. We further Zalla et al.’s argument, advocating for a relational approach to estimating COVID-19 racial inequities that integrates the reciprocal relationship between context and composition through the interaction of places and people over time. To support our argument, we present a series of figures exploring the heterogeneous relationships between places, people, and time, using US county-level publicly available COVID-19 mortality data from February to December 2020 from Johns Hopkins University. Longitudinal and more geographically granular data that allows for disaggregation by person, place, and time will improve our estimation and understanding of inequities in COVID-19.

scholarly journals Visualising population distribution in Australia over time using rapid 3D web graphics libraries

2019 ◽  
Vol 3 (2) ◽  
pp. 29-33
Author(s):  
Oliver Lock ◽  
Chris Pettit
Keyword(s):  
Population Distribution ◽  
Over Time ◽  
3D Web

DemoGraphic

scholarly journals Product Architecture Definition Based Upon Customer Demands

1999 ◽  
Vol 121 (3) ◽  
pp. 329-335 ◽  
Author(s):  
J. S. Yu ◽  
J. P. Gonzalez-Zugasti ◽  
K. N. Otto
Keyword(s):  
Population Distribution ◽  
Probability Distributions ◽  
Cost Savings ◽  
Product Architecture ◽  
Product Portfolio ◽  
Target Values ◽  
Changes Over Time ◽  
The Relationship ◽  
Over Time ◽  
Customer Need

The product portfolio architecture developed by a design team will have a tremendous impact upon customer satisfaction and market acceptance of the set of products offered by the firm. Yet most work in architecture centers around cost savings, manufacturability, and other production-driven concerns. Here, we propose a customer need basis for defining the architecture of a portfolio of products. Customer needs analysis provides a list of requirements for a product to sell. At any moment in time, one can assess a market population to establish target values for product features and represent those targets as probability distributions. Similarly, one can also trace the product through its use over time, and establish a separate set of desired target values, also as a set of distributions. Comparing these two distribution sets for every important customer need can point to the type of architecture a market population desires. When population and time distributions match, feature adjustability is required. When these distributions are different but constant in time, a family of product variants is more appropriate. When the population distribution changes over time, the feature must be isolated so it can be upgraded over time. If the distributions across both time and population are narrow, a single offering will supply the needs of the market. An instant film camera product is used as an example of the relationship between customer need distributions and appropriate product architecture.

A Markovian Entropy-Maximizing Model of Population Distribution

1974 ◽  
Vol 6 (6) ◽  
pp. 693-702 ◽  
Author(s):  
R Lee
Keyword(s):  
Markov Chain ◽  
Stationary Process ◽  
Population Distribution ◽  
Population Densities ◽  
Time Intervals ◽  
Entropy Model ◽  
Regional Modelling ◽  
Distribution Vector ◽  
Biased Estimates ◽  
Over Time

The entropy-maximizing formalism used in urban and regional modelling has typically been applied within a static or equilibrium context. This paper presents a dynamic entropy model of the distribution of population over time. It is initially assumed that a Markov chain adequately represents the residential relocation process. The strategy then involves maximizing the entropy of a Markov chain, subject to suitable constraints, so as to generate least-biased estimates of the Markovian parameters. If a stationary process is assumed, these in turn allow the projection of the probability distribution vector of population densities over successive, equal, time intervals.

scholarly journals Modelling population density over time: how spatial distance matters

2015 ◽  
Vol 51 (4) ◽  
pp. 602-615 ◽  
Author(s):  
Ilenia Epifani ◽  
Rosella Nicolini
Keyword(s):  
Population Density ◽  
Spatial Distance ◽  
Over Time

scholarly journals Characterization of Acute Poisoning in Hospitalized Children in Southwest China

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhu Li ◽  
Li Xiao ◽  
Lin Yang ◽  
Shaojun Li ◽  
Liping Tan
Keyword(s):  
Hospital Stay ◽  
Southwest China ◽  
Population Distribution ◽  
Univariate Analysis ◽  
Length Of Hospital Stay ◽  
Acute Poisoning ◽  
Living Environment ◽  
Hospitalized Children ◽  
Poisoning In Children ◽  
Over Time

Objective: Acute poisoning in children is characterized by regional differences. This study described the basic situation of childhood poisoning in southwest China based on hospitalized cases.Data and Methods: A total of 1,076 acute poisoning cases among hospitalized children admitted to Children's Hospital of Chongqing Medical University from January 2012 to December 2020 were included in this study. Clinical data such as gender, age, living environment, poisonous substance, and cause of poisoning were statistically described. Trends of length of hospital stay, population distribution, poisonous substances, and causes of acute poisoning in the hospitalized children were compared over time.Results: The cohort comprised 588 males and 488 females; 811 cases lived in rural areas and the rest resided in urban areas. Most cases were between early childhood and school age. Poisoning usually occurred at home (973 cases, 90.4%). Pesticides (381 cases, 35.4%) and drugs (275 cases, 25.6%) were the two most common poisonous substances. Two main causes of poisoning were accidental taking (755 cases, 70.2%) and suicide (177 cases, 16.4%). The results of univariate analysis of suicide showed significant correlations among gender, place of residence, age, poisonous substance, and place of suicide (P < 0.001), while living environment (town), age (adolescence), and poisonous substance (pesticide, drug) were independent risk factors (P < 0.05). There was no significant change in the length of hospital stay for poisoning over time. The overall number of hospitalizations presented a decreasing trend, while the number of urban children gradually increased. The proportion of adolescent poisoned children and suicidal children increased in the last 3 years.Conclusion: Optimizing the package and distribution channels of pesticides and drugs, raising safety awareness of children to avoid accidental injuries, and paying attention to children's mental health are measures that are necessary to prevent poisoning in children.

scholarly journals A model of dynamic migration networks: Explaining Turkey's inter-provincial migration flows

2020 ◽  
Author(s):  
Ozan Aksoy ◽  
Sinan Yıldırım
Keyword(s):  
Regression Model ◽  
Estimation Method ◽  
Spatial Distance ◽  
Economic Prosperity ◽  
Multinomial Regression ◽  
Network Characteristics ◽  
Migration Networks ◽  
Multinomial Regression Model ◽  
Migration Flows ◽  
Over Time

In this study we propose a Dirichlet-multinomial regression model and a Bayesian estimation method to analyse dynamic migration networks whereby the nodes are the origins and destinations, and the edges are the number of people migrating between the two over time. We apply this model to analyse 25,632,876 migration instances that take place between Turkey's 81 provinces from 2009 to 2018. We quantify the associations of economic, physical, social, political, and network factors with migration. We find that economic prosperity and population in and the spatial distance between the origin and destination, as well as network characteristics such as reciprocity, province popularity and centrality are important predictors of migration. We also find that electoral distance between provinces and the strength of the AKP (Justice and Development Party) in a province are associated with migration. The proportion of Kurds in a province has no sizeable association with in- and out-migration.

Aspects of the Lognormal Function in the Analysis of Regional Population Distribution

1989 ◽  
Vol 21 (7) ◽  
pp. 961-973 ◽  
Author(s):  
J B Parr ◽  
G J O'Neill
Keyword(s):  
Population Density ◽  
Spatial Structure ◽  
Density Function ◽  
Metropolitan Area ◽  
Population Distribution ◽  
Population Based ◽  
Major Metropolitan Area ◽  
Structure Of Population ◽  
Regional Population ◽  
Over Time

The spatial structure of population within a metropolitan-area-based region is approached via the population density function, in much the same way as has been undertaken for a city or metropolitan area. A particularly appropriate form of the density function is the lognormal, and the broad features of this function are outlined. The concern in the balance of the paper is with certain properties of the lognormal form. These properties are descriptive in nature, but may also be of a predictive character, in the sense that the predicted property can be contrasted with its observed counterpart. Consideration is given first to properties based on density. The population form of the lognormal function is then derived, and properties based on population are examined. Attention is also given to relationships among density-based and population-based properties. An example of the approach and how it may contribute to the analysis of spatial structure, particularly over time, is illustrated with respect to a major metropolitan-area-based region. Last, possible applications of this type of analysis are discussed.

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